Evolving Loop and Recursion Structures in Genetic Programming

As a new paradigm, genetic programming has been successfully applied
to many applications, from regression, circuit design, image
processing and recognition, binary classification to recently
multiclass classification problems. It is recognised as one of the
main paradigm in machine learning and evolutionary computing.

Compared with human written programs, the genetic programs evolved by
GP still have a number of limitations. While human written programs
can have selection structure such as if-then, if-then-else and
switch-case, loop structures such as for-loop, while-loop and
do-while-loop, and other structures such as trees, lists and vectors,
the GP evolved programs usually only have simple selection structure
such as if-then and simple tree structures.

The goal of this project is to investigate new ways of introducing new
structures into GP. We expect that this approach will extend the power
of GP and improve the comprehensibility of genetic programs, and
accordingly turn GP to a widely applicable technology for solving
practical and more difficult problems. This project will focus on
evolution of loop and recursion structure in genetic
programming. Specifically, this project will investigate:

Whether some high level of selection structures such as
if-then-else and switch-case can be evolved in genetic programming;

Whether loop structures such as for-loop, while-loop and do-while
loop can be evolved in genetic programming; and

Whether recursion structures can be evolved in genetic
programming.

The methods developed will be examined on some mathematical problems
and image recognition problems.

A strong background in Java/C/C++ programming and a basic background
in Artificial Intelligence and statistics are required. A good
background in machine learning, and operations research is desired
(COMP307, COMP361).

Please check
http://homepages.ecs.vuw.ac.nz/~mengjie/papers/index.shtml,
http://ecs.victoria.ac.nz/Main/MengjieZhang, and
http://ecs.victoria.ac.nz/Groups/ECRG/ for publications and other
information.